The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

Chen, Jun

Bibliographic data

Monograph

Persistent identifier:: 856566209

Author:: Chen, Jun

Title:: The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

Sub title:: May 23 - 25, 2001, Bangkok, Thailand

Scope:: VI, 434 Seiten

Year of publication:: 2001

Place of publication:: Pathumthani, Thailand

Publisher of the original:: AIT

Identifier (digital):: 856566209

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: PRIMARY SPATIAL CHANGES. Hong SHU, Christopher GOLD and Jun CHEN

Document type:: Monograph

Structure type:: Chapter

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 263 pects of spatio- spatio-temporal me and relative me and spatial hanges present dge is to refine gn time or date as two kinds of 3s are suited for ts of work here. , individual and >n change, size iships between described with ame time, it is two topics are ring object data r rwig, M., R.H. -temporal data imbach, S., P. pes consist of een embedded tically, moving patial extent. It aim at location vehicles, which In constraint- and time are int equality or represents an th space-time med into h(x, nt of an object is treated as a r)>=g(t) means hematic value data models object. The )nstraint-based it the high level of modeling independently of specific representations and storage structures, but the latter is at the low level of logical representations. Anyway, either moving object types or spatio- temporal constraints explicitly reveal only one spatio-temporal semantic, i.e., location change semantic. In a sense of geometry or point-set theory, location as a coordinate point is an atomic unit to describe spatial objects, and location change certainly is the most basic spatial change. However, it is known that point set or point-string or coordinate-sequence data structures not only increase difficulties of data storage and access, but also bring overburden to spatial analysis. This means that much useful semantic information has to be extracted by algorithms or user’s understanding. To overcome these shortcomings, semantically rich data structures like node-arc-polygon and point-edge-triangle with connectivity and neighborhood relationships, are preferred in practice. Again, in a knowledge sense, location change is only one of four spatial primitives (Reginald G. Golledge, 1995), and spatio-temporal semantics should be modeled at multi-levels, not only at the level of coordinate point. Amazingly, direction is being identified as an independent spatial object recently (Shashi Shekhar and Xuan Liu, 1998). Taking the forest management as example, enlarging planting area of a tree species intuitively is a change of area of a tree stand, rather than its location change, and changing tree planting stand from square to strip intuitively is a shape change of a tree stand. The lack of semantics in current data models and presentation of direction object motivate us to study spatio- temporal semantics. In general, time has two kinds of semantic, i.e., time-scale semantic and event-sequence semantic. By these two kinds of time semantic, accordingly M.F. Worboys (1994) developed a spatio-temporal complex model, where time and space are two semantically independent dimensions unified in a mathematical form of complex, and D. J. Peuquet et ai. (1995) designed an event-based spatio-temporal data model TRIAD. To a large extent, event-sequence time semantics or spatial change modeling seems more important than time-scale time semantics or spatio-temporal position modeling. To enrich time semantics of spatial event, we examine existing taxonomies of spatial change in section 2. In section 3, three levels of spatial change (scene change, object change and property change) are identified. In section 4, relevant cognitive understandings are given. In the concluding section, we made a further discussion. Note that terms of event and change are interchangeably used in this paper. 2 PREVIOUS WORK ON CHANGE CLASSIFICATIONS BY DIFFERENT CRITERIA As stated above, event sequence presents human an image of time. In other words, events reveal an inherent time semantic. The structure and types of event imply time semantics. In databases, an event is generally represented with a function of state change. A spatial event is a function of spatial state change. That is, spatial changes indicate spatio-temporal semantics. In the following, we investigate previous work on change classifications based on different criteria. 2.1 The Criterion of Time-varying Patterns Patterns are distribution laws of elements in a set. Time-varying patterns refer to distribution laws of time sequence data spatial or thematic. Basic time-varying patterns have discrete, stepwise and continuous changes. These time-varying patterns are firstly examined by Segev A. and Shoshani A.(1987), who called them time-series types. Afterwards, Yeh, T.S. and B. De Cambray (1993) introduced them into Temporal GIS, and called them behavior functions of data. Formally, given a time sequence of data, {(a1, t1), (a2, t2), .... (an, tn)}, t1<t2<...<tn, we can define three time-varying patterns: • Discrete change. The data value sequence (a1, a2, ..., an) completely depends on observation or measurement, and no computational laws are available. For example, wood product volumes of a tree stand are recorded at different times. • Stepwise change. In the time sequence {(a1, t1) (ai, ti), .... (an, tn)), the data value ai remains constant from an time instant ti through next instant ti+1. For example, tree stand changes (splitting, merging, enlargement or reshaping) are stepwise changes. • Continuous change. The data value ai can be computed by mathematical functions such as a spline function, a linear functions, etc. For example, temperature change in forestry is a kind of continuous changes. In fact, three kinds of change can be viewed as three forms of a time-varying function at different discretization degrees, that is, continuous function for continuous change, segmented function for stepwise change and approximated function with a set of discrete values for discrete change. Meanwhile, discrete change at a small scale can be generalized into continuous change at a larger scale. Anyway, in a semantic modeling sense, three time- varying patterns reveal three time-varying laws or three time semantics, which facilitate human time information comprehension. 2.2 The Joint Criterion of Geometrical Dimensionality and Time-varying Patterns In Nancy J. Yattaw’s thesis, geographic movements are categorized by characteristics of change in space and time. Spatially, geographic phenomena are abstracted with geometrical point, line, area and volume, and spatial changes are categorized into point change, line change, area change and volume change. For simplicity, volume phenomenon is omitted in this paper. Temporally, dynamic phenomena are characterized by continuous, cyclical and intermittent changes. Continuous change indicates that a phenomenon moves uninterruptedly throughout a period of examination. Movement, which is discontinuous during examination and stops periodically, is said to be fluctuating. For cyclical change (periodical fluctuation), the frequency of every movement is predictable and regular. Intermittent change is a fluctuation, which is sporadic or irregular. By combining three kinds of spatial change with three kinds of temporal change, geographic movements are categorized into nine groups, in the mathematical form, {point change, line change, area change) X {continuous change, cyclical change, intermittent change)= {continuous point change, continuous line change, continuous area change, cyclical point change, cyclical line change, cyclical area change, intermittent point change, intermittent line change, intermittent area change), where “X” is Cartesian Product (Nancy J. Yattaw, 1997). It is evident that point, line and area are classified by criterion of geometrical dimensionality. Point, denoted by geometric coordinate (x, y), is a zero-dimensional geometric object without extent. Line, with a certain length and zero width, is a one dimensional geometric object. Area is two-dimensional extended geometric object. In general, we coarsely group time-varying patterns into continuous change and discontinuous change first, then finely group discontinuous changes into cyclical change and intermittent change in terms of whether the frequency of change is predictable. Change with an unpredictable frequency is intermittent, whereas change with a predictable frequency is cyclical. Intermittent change and cyclical change are similar to discrete change and stepwise change respectively in section 2.1. Nancy J. Yattaw's change classification is based on the joint criterion of geometrical dimensionality and time-varying patterns. 2.3 The Joint Criterion of Geometrical Dimensionality and Location Movement It is realized that spatial data is varying over time discretely or continuously. In the course of mobile computation, Sistla, A.P., O. Wolfson et al. (1997) put forward the concept of moving object, whose location is varying over time. In their moving object spatio-temporal data model (MOST), dynamic spatial property (changing location) is represented with a motion vector.

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